| CPC G06Q 50/184 (2013.01) | 17 Claims |
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1. A system comprising:
one or more processors; and
non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
generating, utilizing a machine learning model having parameters configured to assess attributes of intellectual property data, quality scores associated with multiple entities, individual ones of the quality scores indicating one or more of:
a degree of coverage associated with intellectual property assets of the multiple entities;
a degree of opportunity for expanding coverage of the intellectual property assets of the multiple entities; and
a degree of exposure associated with the intellectual property assets of the multiple entities;
storing first data representing the quality scores for the multiple entities along with indicators of the intellectual property assets of the multiple entities;
receiving feedback data indicating outcomes of transactions associated with the intellectual property assets of the multiple entities;
generating a training dataset from the feedback data;
training, utilizing the training dataset, the machine learning model such that an updated machine learning model is generated that includes updates to at least one of the parameters;
receiving, via a secure user interface, a request to evaluate intellectual property assets associated with an entity;
generating a first quality score for the intellectual property assets of the entity utilizing the updated machine learning model;
determining a portion of an intellectual property portfolio of a selected entity attributable to a technology category that is also associated with the intellectual property assets of the entity;
generating a second quality score based at least in part on the portion of the intellectual property portfolio of the selected entity; and
generating a comparison between (1) the portion of the intellectual property portfolio of the selected entity and (2) the intellectual property assets of the entity, the comparison based at least in part on the first quality score and the second quality score;
determining a first weighting value to apply to first parameters associated with the degree of coverage, the first parameters indicating at least a quantity of the intellectual property assets, a breadth of the intellectual property assets, and a market alignment of the intellectual property assets to at least one market associated with the entity;
determining a second weighting value to apply to second parameters associated with the degree of opportunity, the first parameters indicating at least a number of intellectual property applications associated with the entity, a first intellectual property application filing velocity associated with the entity, and a second intellectual property application filing velocity associated with entities in the market;
determining a third weighting value to apply to third parameters associated with the degree of exposure, the third parameters indicating at least an intellectual property validity probability and litigation metrics associated with the intellectual property assets;
based at least in part on the feedback data, adjusting the first weighting value, the second weighting value, and the third weighting value; and
wherein generating the updated machine learning model is based at least in part on adjusting the weighting value, the second weighting value, and the third weighting value.
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